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Record W2496579398 · doi:10.52034/lanstts.v1i.18

Information flow in excerpts of two translations of Mme Bovary

2021· article· en· W2496579398 on OpenAlex
Alexandre Sévigny

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLinguistica Antverpiensia New Series – Themes in Translation Studies · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCognitive grammarLinguisticsComputer scienceGrammarInformation flowCognitionRule-based machine translationCogConstruction grammarArtificial intelligenceNatural language processingPsychologyPhilosophy

Abstract

fetched live from OpenAlex

This article explores how information is accumulated and collated in a cog-nitively realistic fashion in two very short excerpts of translations of Flaubert ’s ‘Mme Bovary’. The approach taken is a formal cognitive linguistic one using Discourse Information Grammar (DIG), a theory of grammar based on the intuitive idea that texts are understood by the reader incrementally, in a left-to-right fashion. Thus, a cognitive pragmatic approach is taken to the study of the excerpts, highlighting how much information is accumulated as the reader develops an understanding of the text in question. The analysis discusses the differences in the build-up of information in the source text and in its translations. The conclusion indicates that translation studies contribute much to the development of formal linear cognitive linguistic theories.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.749
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.072
GPT teacher head0.327
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it